Abstract
As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. This paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. With this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. In particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2007 ACM Symposium on Applied Computing |
| Pages | 506-510 |
| Number of pages | 5 |
| DOIs | |
| Publication status | Published - 2007 Oct 18 |
| Event | 2007 ACM Symposium on Applied Computing - Seoul, Korea, Republic of Duration: 2007 Mar 11 → 2007 Mar 15 |
Publication series
| Name | Proceedings of the ACM Symposium on Applied Computing |
|---|
Other
| Other | 2007 ACM Symposium on Applied Computing |
|---|---|
| Country | Korea, Republic of |
| City | Seoul |
| Period | 07/3/11 → 07/3/15 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
Cite this
}
Personalized ranking : A contextual ranking approach. / You, Gae Won; Hwang, Seungwon.
Proceedings of the 2007 ACM Symposium on Applied Computing. 2007. p. 506-510 (Proceedings of the ACM Symposium on Applied Computing).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Personalized ranking
T2 - A contextual ranking approach
AU - You, Gae Won
AU - Hwang, Seungwon
PY - 2007/10/18
Y1 - 2007/10/18
N2 - As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. This paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. With this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. In particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.
AB - As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, in the context of text retrieval, in contrast to traditional web search engines retrieving the same results for all users, major commercial search engines are starting to support personalization, improving the search quality by adapting to the user-specific retrieval contexts, e.g., prior search history or other application contexts. This paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt context-sensitive ranking model to formalize personalization as a cost-based optimization over context-sensitive rankings collected. With this formalism, personalization is essentially retrieving the context-sensitive ranking matching the specific user's retrieval context and generating a personalized ranking accordingly. In particular, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over real-life data validate both the effectiveness and efficiency of our framework.
UR - http://www.scopus.com/inward/record.url?scp=35248897969&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=35248897969&partnerID=8YFLogxK
U2 - 10.1145/1244002.1244119
DO - 10.1145/1244002.1244119
M3 - Conference contribution
SN - 1595934804
SN - 9781595934802
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 506
EP - 510
BT - Proceedings of the 2007 ACM Symposium on Applied Computing
ER -